How might climate change alter California’s risk of floods in the future? Findings from this project suggest that flooding will become more intense in the San Joaquin and (to a lesser extent) Sacramento watersheds by the end of the century, irrespective of whether the climate becomes wetter or drier. More intense flooding appears to be a consequence of several factors—principally bigger storms, more frequent big storms and more days of precipitation falling as rain instead of snow. Moister winter soils, which may be too saturated to absorb added water, also contribute to flooding in some areas.

The sites of cardiac excitation-contraction (E-C) coupling are composed of sarcoplasmic reticulum (SR)-localized calcium release channels, known as ryanodine receptors (RyRs), coupled to voltage-gated L-type calcium channels (LTCCs) on the sarcolemma in junctional membrane micro- domains termed "couplons". Mounting evidence suggests that the dysregulation of calcium fluxes within these domains is critical in the pathogenesis of heart failure. Despite their essential role in the maintenance of normal myocardial excitation and contractility, our quantitative understanding of couplons is greatly limited due to the formidable technical challenge of imaging and exploring the structure-function relationship of the E-C coupling site. In the work presented here, I developed novel two- and three-dimensional approaches for in-resin, correlated super-resolution fluorescent light microscopy (LM) and electron microscopy (EM) to quantify the distribution of key E-C coupling molecules and reveal their association with membranous organelles in mammalian cardiomyocytes. The imaging of resin-embedded sections with stochastic optical reconstruction microscopy (STORM) was immediately followed by ultrastructural mapping using scanning or transmission EM. Three-dimensional EM data were reconstructed with both array and EM tomography. Correlated imaging using STORM and scanning EM across multiple cells revealed that while most RyRs were mapped within couplons, 21.0 ± 4.5% (n=6) of RyRs were non- junctional. LTCCs were found in couplons, and most NCXs were confined to the non-junctional subdomain of the sarcolemma. The exact localizations of junctional and non- junctional RyRs were further elucidated using correlated STORM and EM tomography, confirming that RyR signals colocalized with "feet" structures visible in couplons at the EM level. Interestingly, a significant population of non-junctional RyRs was found at the inter-membrane junctions between the network SR and the outer membrane of mitochondria. This technique was further applied to study the ultrastructural remodeling and associated RyR reorganization in genetically engineered junctophilin 2 knockout mice, a disease model which mimics abnormal E-C coupling observed in heart failure. The approach presented in this dissertation has facilitated the expansion of our understanding of ion-channel organization in the cardiomyocyte E-C coupling pathway and will pave the way for detailed models of the molecular mechanisms that lead to reduced myocardial contractility in heart failure

We derive competitive tests and estimators for several properties of discrete distributions, based on their i.i.d. sequences. We focus on symmetric properties that depend only on the multiset of probability values in the distributions and not on specific symbols of the alphabet that assume these values. Many applications of probability estimation, statistics and machine learning involve such properties. Our method of probability estimation, called profile maximum likelihood (PML), involves maximizing the likelihood of observing the profile of the given sequences, i.e., the multiset of symbol counts in the sequences. It has been used successfully for universal compression of large alphabet data sources, and has been shown empirically to perform well for other probability estimation problems like classification and distribution multiset estimation. We provide competitive estimation guarantees for the PML method for several such problems. For testing closeness of distributions, i.e., finding whether two given i.i.d. sequences of length n are generated by the same distribution or by two different ones, our schemes have an error probability of at most sqrt(delta) * exp(7n̂(2/3)) whenever the best possible error probability is delta <= exp(-14n̂(2/3)). The running time of our scheme is O(n). We do not make any assumptions on the distributions, including on their support size. In terms of sample complexity, this implies that if there is a closeness test which takes sequences of length n and has error probability at most delta, our tests have the same error guarantee on sequences of length n' = O(\max{n̂3/ loĝ3(1/4 delta),n}). Similar results are implied for the related problem of classification. For finding the probability multiset of a discrete distribution using a length-n i.i.d. sequence drawn from it, we show the following guarantee for the PML-based estimator. For any class of distributions and any distance metric on their probability multisets, if there is an estimator that approximates all distributions in this class to within a distance of epsilon > 0 with error probability at most delta <= exp(-6n̂(1/2)), then the PML estimator is within a distance of 2 * epsilon with error probability at most delta * exp(6n̂(1/2)). Equivalently, the PML estimator approximates distributions to within a distance of 2 * epsilon with error probability delta using sequences of length n' = O(\max{n̂2/loĝ2(1/4 delta),n}). Thus, this estimator is competitive with other estimators, including the one by Valiant et al. that approximates distributions of superlinear support size k = O(epsilon̂(2.1) * n * log(n)) to within a relative earthmover distance of epsilon and whose error probability can be shown to be at most exp(-n̂(0.9)). However, unlike the case of closeness testing, we do not yet have efficient schemes for computing the PML distribution. We extend the results for PML for distribution multiset estimation to two related problems of estimating the parameter multiset of multiple distributions or processes. These include the problems of estimating the multiset of success probabilities of Bernoulli processes, and the multiset of means of Poisson distributions

My dissertation research focuses on the construction of self and identity by Indian immigrant professional and semi-professional women who live and work in the San Francisco Bay Area. I have made an ethnographic study of the manner in which economic mobility and professional achievement remake gender, race, and class relations. The major issues are: What are the selves and identities of professional Indian women? How is continuity of selves and identities accomplished when individuals constantly shuttle between starkly different ethnoscapes of American workplace, Indian immigrant home, and transnational ideoscapes of ethnic belonging and cross-border ties? Indian immigrants in the San Francisco Bay Area have often been defined as a model minority. Indian immigrant women who have achieved entry into the current post-industrial service-related and technology -based economy in the Bay Area value the capital accumulation, status transformation, socio-economic autonomy, and renegotiation of familial gender relations that are made possible by their employment. However, this quintessential American success story conceals the psychic costs of uneasy Americanization, social misrecognition, long drawn out gender battles, and incessant cross-cultural journeys of selves and identities. Americanization increases with the length of residence in the United States and duration of participation in the American labor force. However, despite their concerted attempts at being "American", my subjects continue to be viewed as "Indians", that is, as representatives of a foreign and exotic culture. Essentialization, whether positive or pejorative, causes psychological dissonance. My respondents are called upon to "speak for" Indian culture precisely when they are drifting away from old Indian habits and adopting new American ways. Nostalgia for the "homeland", as well as, "misrecognition" as "Indian" (rather than "Indian American") leads to a partial abandonment of the path to assimilation, and hence, it results in the reproduction of an Indian diasporic identity that is activated as and when needed. Thus, the Indian immigrant home becomes a principal site for the recomposition of Indian culture, and transnational ties to the "home-country" are strengthened. Code-switching back and forth between the performances of their dual American and Indian identities, my subjects have formulated a unique response to the contradictions in the expectations of American society and workplace on one hand, and the Indian immigrant home and community on the other

This dissertation presents techniques to improve the performance of both coherent as well as non-coherent wireless communication systems via optimizing symbol timing, frequency spacing and by making efficient SNR estimations. We show that some of the design choices made in traditional systems are not optimal and demonstrate the gains that may be achieved by making unconventional, but judicious, choices for these parameters. We start in the area of coherent multi-antenna communications where we introduce an offset between the symbol boundaries of the transmitted waveforms from the different antennas and show that this improves performance in comparison to the traditional symbol aligned transmission. For this modified system, we derive various optimal receivers such as maximum likelihood (ML), best linear unbiased estimator (BLUE), minimum mean squared error (MMSE), and zero forcing (ZF) receivers and show that they outperform the equivalent receiver for the system with aligned symbol boundaries. In some system configurations, the performance gain is close to 2dB. The design methodology for a new symbol pulse shape that increases the performance even more is also presented. Next, we extend the study of SNR estimation from the previously published results of a data aided (DA) single antenna system to the non-data aided (NDA) model and also to systems with multiple antennas (MIMO). In both these cases, we have derived the Cramér- Rao lower bound (CRLB) as well as ML estimators that achieve or perform very close to the CRLB. For MIMO systems we define the SNR and then derive the CRLB and the ML estimators for both the DA as well as the NDA data model. We show that previously published results for single antenna systems are a special case of our general solution. The proposed SNR estimation techniques are demonstrated in a patented algorithm to detect the onset of non-linearity in a remote transmitter by dithering the power of transmitted bursts and estimating the difference in the received SNR. For non-coherent systems we show that the performance of multi-tone M-ary frequency shift keying (MT-MFSK) modulation may be significantly improved if, instead of the usual choice of mutually orthogonal tones, non-orthogonal tones are used. In some system configurations, the proposed system can lead to a 4-fold increase in system capacity. The channel capacity, as well as the performance gains of systems using practical receivers such as ML, least squared (LS) error, and compressed sensing (CS) are demonstrated for both flat and frequency selective channels. Many more choices of spectral efficiency are achievable by the non-orthogonal system, thus enabling the system to adapt to changing link SNR and send data at the optimum spectral efficiency. In order to make this practical, we derive the CRLB and ML estimators for SNR estimation for non-orthogonal MT-MFSK in both the DA as well as the NDA data model

We explicitly compute the Picard groups of the projectivoid line and its corresponding characteristic $p$ tilt. The desire to generalize this to higher-dimensional projectivoid spaces, as well as other perfectoid spaces, leads us to ask whether a GAGA principle holds between perfections of analytifiable schemes in characteristic $p$, and corresponding completed perfections of their adic analytifications.